Envisioning desirable futures in small-scale fisheries: a transdisciplinary arts-based co-creation process
Bibliographic record
Abstract
Despite the critical importance of small-scale fisheries for food security and well-being and the role of fishers as stewards of aquatic ecosystems, their future is uncertain. Tackling narratives that portray small-scale fisheries as obsolete, disparate, and inefficient requires collectively imagining and articulating new, creative, and inspiring narratives that reflect their real contributions and enable transformative futures. Drawing on a transdisciplinary country-level case study, we analyze the process and outcomes of co-creating desirable, plural, and meaningful visions of the future for small-scale fisheries in Uruguay. Using an arts-based approach and leveraging the agency of emerging innovative initiatives throughout the country, different food system actors (fish workers, chefs, entrepreneurs) and knowledge systems (local, experience-based, and scientific) were engaged in a creative visioning process. The results of this arts-based co-creation process include (1) a series of desirable visions and narratives, synthesized into an artistic boundary object; and (2) the stepping stones to a transformative space for collective reflection, learning, and action. Although the artistic boundary object has proven instrumental among multiple and diverse participants, the transformative space encouraged academic and non-academic participants to plan collective actions and to feel more confident, motivated, and optimistic about the future of small-scale fisheries in Uruguay. With this paper we provide a tool, a platform, and a roadmap to counter the dominant bleak narrative, while also communicating the elements that constitute desirable futures for small-scale fisheries in Uruguay. On a broader scale, our contribution reinforces the emerging narrative of the key role that small-scale fisheries have, and will play, in local and global food systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.020 | 0.033 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".